CBIM-Medical-Image-Segmentation

This repo is a PyTorch-based framework for medical image segmentation, whose goal is to provide an easy-to-use framework for academic researchers to develop and evaluate deep learning models. It provides fair evaluation and comparison of CNNs and Transformers on multiple medical image datasets.

News

We've released a new version paper of UTNetV2, including more experiments and analysis, see in UTNetV2

Supports for BCV and LiTS dataset have been updated.

Features

• Cover the whole process of model design, including dataset processing, model definition, model configuration, training and evaluation.
• Provide SOTA models as baseline for comparison. Model definition, training and evaluation code are simple with no complex code encapsulation.
• Provide models, losses, metrics, augmentation and etc. for 2D, 3D data, multiple modalities and multiple tasks.
• Optimized training techniques for SOTA performance.

Supporting models

• UTNetV2 (Official implementation)
• UNet. Including 2D, 3D with different building block, e.g. double conv, Residual BasicBlock, Bottleneck, MBConv, or ConvNeXt block.
• UNet++
• Attention UNet
• Dual Attention
• TransUNet
• SwinUNet
• UNETR
• VT-UNet
• More models are comming soon ...

Supporting Datasets

• ACDC Cardiac MRI
• BCV Abdomen Organ CT
• LiTS Liver and Tumor CT
• More datasets are comming soon

Usage

We provide flexible usage. If you just want to use the models in your own framework, you can directly find the corresponding models in the model/ folder and use them in your own framework. For the definition of specific models, please refer to model/utils.py in the get_model function. The models we provide do not have complex dependencies and encapsulation. The modules used by each model are defined in its own xxx_utils.py file. For example, the model definition of UNet only depend on unet.py, unet_utils.py and conv_layers.py.

If you want to use our framework, please follow below steps.

Install requirements

Create a new virtual environment and install all dependencies by:

pip install -r requirement.txt

Data preparation

Download the origin dataset from their corresponding official website.

Enter the dataset_conversion fold and find the dataset you want to use and the corresponding dimension (2d or 3d)

Edit the src_path and tgt_path the in xxxdataset.py, where the src_path is the path to the origin dataset, and tgt_path is the target path to store the processed dataset.

Then, python xxxdataset.py

After processing is finished, put the processed dataset into dataset/ folder or use a soft link.

Configuration

Enter config/xxxdataset/ and find the model and dimension (2d or 3d) you want to use. The training details, e.g. model hyper-parameters, training epochs, learning rate, optimizer, data augmentation, etc., can be altered here. You can try your own congiguration or use the default configure, the one we used in the UTNetV2 paper, which should have a decent performance. The only thing to care is the data_root, make sure it points to the processed dataset directory.

Training

After the data, model and training strategy are configured, we can start training. Several arguments can be parsed in command line. You need to specify the model, the dimension, the dataset, whether use pretrain weights, batch size, and the experiment name. Our code will find the corresponding configuration for training. Here is an example to train 3D UTNetv2 on acdc.

python train.py --model utnetv2 --dimension 3d --dataset acdc --batch_size 3 --unique_name acdc_3dutnetv2 --gpu 0

The training process will be logged by tensorboard, including training loss, testing performance, and etc. You can find it in the log/unique_name folder and use it to monitor the training process. After the training is done, the result of cross-validation will be stored in the exp/exp_unique_name folder.

To Do

Add multi-GPU parallelism support.

Add MSD dataset support.

We'll continously maintain this repo to add more SOTA models, and add more dataset support.

Performance comparison results of the supported models and dataset

Hope this repo can serves as a solid baseline for the future medical imaging model design.

Citation

If you find this repo helps, please kindly cite our paper, thanks!

@inproceedings{gao2021utnet,
  title={UTNet: a hybrid transformer architecture for medical image segmentation},
  author={Gao, Yunhe and Zhou, Mu and Metaxas, Dimitris N},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={61--71},
  year={2021},
  organization={Springer}
}

@misc{gao2022datascalable,
      title={A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark}, 
      author={Yunhe Gao and Mu Zhou and Di Liu and Zhennan Yan and Shaoting Zhang and Dimitris N. Metaxas},
      year={2022},
      eprint={2203.00131},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}